Department of Biostatistics

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fdominic @hsph.harvard.edu November	28	2012 From:	Francesca	Dominici,	Scott	L	Zeger,	Holly	Janes,	Sonja	Greven To:	Bryan	Hubbel l ,	Jason	Sack,	EPA

Reply to: additional information to	EPA	for	these two studies

1) Janes	H,	Dominici
, Zeger	SL	(2007)	Trends	in	Particulate	Matter	and	Mortality	in	113	U.S.	Counties,

2000

- 2002:	Evidence	on	the	Long	Term	Effects	of	Air	Pollution. Epidemiology, 18:	416

- 423	(with discussion) 2) Greven	S,	Dominici	F,	Zeger	S L (2011):	An	Approach	to	the	Estimation	of	Chronic	Air	Pollution Effects	Using	Spatio

- Temporal	Information. Journal	of	the	American	Statistical	Association, 106(494):	396

– 406.

The se	two	studies targeted	a	very	specific	question:	whether	there	is	an	assoc iation	between	month

- to

month	variations	in	mortality	rates	and	month	to	month	variations	in	the	average	PM2.5	for	the	previous 12	months	( global	effect )	in	the	Medicare	population. The	fact	that	this	particular	study	does	not	find	an
association	at	this	s pecific	time	scale	of	variation	of	exposure	to	air	pollution	and	for	the	study	population considered	cannot	lead	to	a	conclusion	that	all	the	other	epidemiological	studies	are	confounded	and	that air	pollution	is	not	causally	linked	to	mortality.	In	this	d ocument	we	summarize	our	rationale	for	making	this

statement.

In	these	studies we	decompose	the global	effects into	two	parts:	1)	the	association	between	the	national average	trend	(NAT)	in	the	monthly	PM2.5	levels	averaged	over	the	previous	12	months	and the	national average	trend	(NAT)	in	monthly	mortality	rates	( national	effect );	and	2)	the	association	between	the deviation	of	the	community

- specific	trend	from	the	NAT	of	PM2.5	and	the	deviation	of	the	community

specific	trend	from	the	(NAT)	of	mortality rates	( local	effect ).	We	decompose	the	global	effect	into	a national	effect	plus	a	local	effect	because	we	hypothesize	that	the	national	effect	is	more	likely	to	be affected	by	unmeasured	confounding	than	the	local	effect.	We	also	argue	that	if	there	are large	differences between	the	local	and	the	national	effects	then	the	global	effect	should	not	be	reported	without	a	more	in depth	investigation	of	confounding.	We	also	acknowledge	in	the	papers	that	differences	between	the	local and	national	effects	might be	due	to	measurement	error	and	not	necessarily	unmeasured	confounding.

Our	results	do	not	invalidate	previous	epidemiological	studies :		In	Janes	et	al	and	Greven	et	al,	both	using Medicare	data,	we	did	not	find	evidence	of	a	local	effect	and	we	instead found	evidence	of	a	national effect.		Although	these	results	call	for	additional	investigation	of	why	we	found	these	differences	between the	local	and	the	national	effects,	these	results	do	not	invalidate	results	of	the	other	cohort	and	multi

- site
time	se ries	studies.		We	summarize	below	why: FRANCESCA DOMINICI , PhD

Professor of Biostatistics

Associate Dean for Information Technology1. We	eliminate	spatial	information,	purposely .	Our	studies,	on	purpose,	discard	all	the	spatial variation	in	air	pollution	and	mortality	that	is	available	in	the	data.		This	is	the	main	and	only
information	used	in	the cohort	studies.	Also,	our	studies	cannot	invalidate	the	time	series	studies, because	they	also	do	not	rely	on	the	day

- to

- day	information.	Again,	our	studies	only	focus	on associations	at	monthly	temporal	scales	and	include	a	fixed	effect	for	county	in	the regression model	which	purposely	eliminate	the	spatial	information	when	estimating	the	global	effect.	At	the other	end,	the	cohort	studies	by	Pope	et	al 2009	NEJM ,	rely	almost	entirely	on	spatial	information and	they	have	developed	approaches	to	adjust	for measured	and	unmeasured	confounding. In Table	1	of	Janes	et	al,	the	percentage	of	the	variability	in	their	PM 2.5 variable	is	broken	down	by space,	time,	and	space	by	time	components.		We	found	that	over	90%	of	the	variance	in	this	data	is
attributable	to spatial	variation	(which	we	ignore),	and	roughly	only	5%	attributable	to	the	space	by time	component	(which	is	what	we	used	in	our	papers).	Thus,	when	one	considers	that	this	wealth of	information	is	not	accounted	for	in	that	study,	it	is	not	as	surprising that	we	see	vastly	different
estimates	of	the	PM 2.5 /mortality	relationship	than	in	other	studies	that	do	exploit	that	variability.

2. Both	the	local	and	the	national	effects	can	be	affected	by	unmeasured	confounding .	We	do hypothesize	that	the	national	eff ect	is	more	likely	to	be	affected	by	unmeasured	confounding	than the	local	effect.	However	the	unmeasured	confounding	for	the	local	scale	cannot	be	excluded either.	For	example,	the	national	association	can	be	confounded	by	a national	downward	trend	in

smo king	that	causes	a	reduction	in	mortality.		As	PM2.5	and	mortality	are	both	trending downwards	nationally,	not	having	a	good	measure	of	the	effect	of	smoking	in	the	model	would cause	an	upward	bias	in	the	national	association.	An	example	that	would	cause	t he	local	association to	be	biased	upwards	would	be	if	communities	that	were	very	'health	conscious	and environmentally	friendly'	and	thus	reduced	their	air	pollution	more	than	the	average	community would	also	improve	on	other	health

- related	measures	such	a s	smoking	and	obesity,	causing	drops in	mortality	larger	than	in	the	average	community,	even	without	the	effects	of	the	reduce	in	air pollution. On	the	other	hand,	an	example	that	would	cause	a	downward	bias	in	the	local
association	would	be	local	manufact uring	activity.	For	example,	suppose	a	new	plant	is	built	that employs	3,000	workers	and	has	a	positive	influence	on	the	economic	productivity	of	its	local	area. The	improved	economy	causes	reduced	mortality	relative	to	the	regional	trend,	but	also	increas es pollution	relative	to	the	regional trend. Such	a	phenomena	would	shift	the	local	association	toward
zero.	As	individual

- and	location

- level	information,	in	contrast	to	cohort	studies,	is	very	limited	for the	Medicare	data,	we	cannot	exclude	any	of	these potential	sources	of	confounding	in	our	studies.

3. Another	important	driver	of	the	potential	differences	between	the	local	and	the	national	effects
could	be	measurement	error .	We	expect	that	measurement	error	could	affect	both	these
associations	but	it	is hard	to	anticipate	which	one	is	more	biased. If	the	exposure	is	measured	with error,	as	we	know	is	the	case,	one	of	the	two	components	might	be	more	precise	than	the	other. Because	the	national	scale	component	is	an	average	of	more	independent	monitors	acr oss	a broader	geographic	extent,	its	"measurement	error"	(including	local	geographic	variations	and	pure device	measurement	error)	might	be	expected	to	be	smaller,	relative	to	the	size	of	the	PM	signal. The	local	component	might	have	greater	measurement	er ror	as	a	proportion	of	its	signal,	and	this might	bias	its	coefficients,	probably	toward	the	null. More	work	is	needed	in	this	area.4. There	might	not	be	an	association	at	the	time	scale	and	lag	investigated	in	our	studies. Further,	it	is an	open	question	as to	what	lag	the	“monthly”	PM 2.5 levels	should	be	calculated	at.		In	the	Janes paper	this	is	calculated	as	the	12	and	24

- month	averages	of	PM 2.5 up	to	and	including	the	current month.		It	is	then	assumed	that	that	calculated	exposure	is	associated	with	the current	month’s observed	deaths.		It	may	be	the	case	that	the	exposure	period	does	not	stretch	that	far	back,	and	is in	fact	over	a	shorter	window.		In	Janes	et	al,	the	authors	acknowledge	that	this	is	a	question	that warrants	further	research	(see	also	S chwartz	et	al	EHP	2008).	Thus,	it	is	entirely	possible	that	these papers	are	looking	for	an	association	at	a	timescale	for	which	no	association	truly	exists.

In	summary,	in	Janes et	al	2007	and	Greven	et	al	2011

, w e examined in	unbiased	fashion,	the	epidemiologic evidence	relevant	to	estimating	the	effect	of	chronic	air	pollution	exposure	on	survival. We	h ave	published all	of	our	findings	without	regard	to	the	outcome. None	of	the	investigators	have	a	financial	conflict	of

interest.

Francesca	Dominici,	PhD Associate	Dean	of	Information	Technology
Professor	of	Biostatistics
Harvard	School	of	Public	Health ,	Boston,	MA

Scott	L.	Zeger,	PhD
Vice	Provost	for	Research The	Johns	Hopkins University ,	Baltimore,	MD

Sonja	Greven,	PhD
Assistant	Professor Department	of	Statistics Ludwig

- Maximilians

- Universität	München Ludwigstraße	33 Munich, Germany

Holly	Janes,	PhD Affiliate	Assistant	Professor,	Biostatistics
Fred	Hutchinson	Cancer	Research Center University	of	Washington ,	Seattle,	WA